ABSTRACT The objective of the research is to analyze the ability of the artificial neural network model developed to forecast the credit risk of a panel of Italian manufacturing companies. In a theoretical point of view, this paper introduces a litera-ture review on the application of artificial intelligence systems for credit risk management. In an empirical point of view, this research compares the architecture of the artificial neural network model developed in this research to an-other one, built for a research conducted in 2004 with a similar panel of companies, showing the differences between the two neural network models.
Cite this paper
nullV. Pacelli and M. Azzollini, "An Artificial Neural Network Approach for Credit Risk Management," Journal of Intelligent Learning Systems and Applications, Vol. 3 No. 2, 2011, pp. 103-112. doi: 10.4236/jilsa.2011.32012.
 T. W. Anderson, “An Introduction to Multivariate Statistical Analysis,” Wiley, New York, 1984.
 W. R. Dillon and M. Goldstein, “Multivariate Analysis Methods and Applications,” Wiley, New York, 1984.
 D. J. Hand, “Dis- crimination and Classification,” Wiley, New York, 1981.
 D. F. Morrison, “Multivariate Statistical Methods,” McGraw-Hill, New York, 1990.
 R. A. Johnson and D. W. Wichern, “Applied Multivariate Statistical Analysis,” 4th Edition, Prentice-Hall, Upper Saddle River, 1998.
 K. Y. Tam and M. Kiang, “Managerial Applications of Neural Networks: The Case of Bank Failure Predictions,” Management Science, Vol. 38, No. 7, 1992, pp. 926-947.
 T. S. Lee, C. C. Chiu, C. J. Lu and I. F. Chen, “Credit Scoring Using the Hybrid Neural Discriminant Technique”, Expert System with Applications, Vol. 23, No. 3, 2002, pp. 245-254.
 E. I. Altman, G. Marco and F. Varetto, “Corporate Distress Diagnosis: Comparisons Using Linear Discriminant Analysis and Neural Networks (the Italian Experience),” Journal of Banking and Finance, Vol. 18, No. 3, 1994, pp. 505-529.
 W. Zhang, Q. Cao and M. J. Schniederjans, “Neural Network Earnings per Share Forecasting: A Comparative Analysis of Alternative Methods,” Decision Science, Vol. 35, No. 2, 2004, pp. 205-237.
 Z. Huang, H. Chen, C. J. Hsu, W. H. Chen and S. Wu, “Credit Rating Analysis with Support Vector Machines and Neural Networks: A Market Comparative Study,” Decision Support System, Vol. 37, No. 4, 2004, pp. 543-558.
 P. Ravi Kumar and V. Ravi, “Bankruptcy Prediction in Banks and Firms via Statistical and Intelligent Techniques-A Review,” European Journal of Operational Research, Vol. 180, No. 1, 2007, pp. 1-28.
 E. Angelini, G. Tollo and A. Roli, “A Neural Network Approach for Credit Risk Evaluation,” The Quarterly Review of Economics and Finance, Vol. 48, No. 4, 2008, pp. 733-755.
 N. Chauhan, V. Ravi and K. Chandra, “Differential Evolution Trained Wavelet Neural Networks: Application to Bankruptcy Prediction in Banks,” Expert System with Applications, Vol. 36, No. 4, 2009, pp. 7659-7665.
 N. C. Hsieh and L. P. Hung, “A Data Driven Ensemble Classifier for Credit Scoring Analysis,” Expert Systems with Applications, Vol. 37, No. 1, January 2010, pp. 534-545. doi:10.1016/j.eswa.2009.05.059
 J. A. Anderson and E. Rosenfeld, “Neurocomputing: Foundations of Research,” MIT Press, Cambridge, 1988.
 W. McCulloch and W. Pitts, “A Logical Calculus of Ideas Immanent in Nervous Activity,” Bulletin of Mathematical Biophysics, Vol. 5, No. 1-2, 1943, pp. 99-115.
 V. Pacelli, “Un Modello Interpretativo Delle Dinamiche dei Corsi Azionari delle Banche: Elaborazione Teorica e Applicazione Empirica,” Banche e Banchieri, No. 6, 2007.
 D. Summo and M. Azzollini, “L’Analisi Discriminante e la Rete Neurale per la Previsione delle Insolvenze Azi- endali: Analisi Empirica e Confronti,” Quaderni di Dis- cussione, No. 24, Istituto di Statistica e Matematica Università degli studi di Napoli “Parthenope,” 2004.
 V. Pacelli, “An Intelligent Computing Algorithm to Analyze Bank Stock Returns,” In: Huang et al. Eds. Emerging Intelligent Computing Technology and Applications, Lectures Notes on Computer Sciences, No. 5754, Springer Verlag, New York, 2009, pp. 1093-1104.